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This study presents a detailed theoretical assessment of the information content of passive polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses the covariance of state parameters across their entire parameter space, hence favoring a more correct interpretation of inversion algorithms and the optimal design of their state vectors. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing particulates (LAPs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAPs in the VIS–NIR and to grain size at different depths in the snowpack in the NIR–SWIR. With adequate viewing geometries, mono-angle measurements of total reflectance in the VIS–SWIR (akin to those of the Moderate Resolution Imaging Spectroradiometer, MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS–NIR provides information on grain shape and microscale roughness. The simultaneous sensitivity in the VIS–NIR to both aerosols and snow-embedded impurities can be disentangled by extending the spectral range to the SWIR, which contains information on aerosol optical depth while remaining essentially unaffected when the same particulates are mixed with the snow. Multi-angle polarimetric observations can therefore (i) effectively partition LAPs between the atmosphere and the surface, which represents a notorious challenge for snow remote sensing based on measurements of total reflectance only and (ii) lead to better estimates of grain shape and roughness and, in turn, the asymmetry parameter, which is critical for the determination of albedo. The retrieval uncertainties are minimized when the degree of linear polarization is used in place of the polarized reflectance. The Sobol indices, which are the main metric for the GSA, were used to select the state parameters in retrievals performed on data simulated for multiple instrument configurations. Improvements in retrieval quality with the addition of measurements of polarization, multi-angle views, and different spectral channels reflect the information content, identified by the Sobol indices, relative to each configuration. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors. They can also aid flight planning activities, since the optimal exploitation of the information content of multi-angle measurements depends on the viewing geometry. The better characterization of surface and atmospheric parameters in snow-covered regions advances research opportunities for scientists of the cryosphere and ultimately benefits albedo estimates in climate models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Retrieving the physical properties and water content of marine aerosols requires understanding the links between the particles' optical and microphysical properties. By using a morphologically realistic model with varying salt mass fractions fm, describing the transition from irregularly shaped, dry salt crystals to brine‐coated geometries, optical properties relevant to polarimetric remote sensing are computed at wavelengths of 532 and 1,064 nm. The extinction cross section and its color ratio depend on particle size, but are insensitive to changes in fm; thus, measured extinction coefficients at two wavelengths contain information on both particle number and size. The lidar ratio's dependence on both size and wavelength has implications for inverting the lidar equation. The results suggest that active observations of the backscattering cross section's color ratio and the depolarization ratio, as well as, passive observations of the degree of linear polarization offer avenues to obtain the water content of marine aerosols. Plain Language Summary: Salt aerosols ejected into air from the ocean surface are the most abundant type of particles in the atmosphere. Clouds form by condensation onto these particles, which, in turn, reflect, absorb, and emit light and thermal radiation, thereby influencing the climate system. To better describe these processes in models, one needs to monitor salt aerosols from satellites. The main difficulty is to obtain information on the particles' concentration, size, and water content from satellite measurements that can only observe light scattered by the aerosols. In this study a model is being employed to study how the shape, size, and composition of marine aerosols impact their light‐scattering properties. The results suggest that the extinction of light measured at two wavelengths provides a robust method for obtaining the concentration and size of the particles. The water content can be obtained from observing the intensity of backscattered laser light at two wavelengths, or the change in polarization of the backscattered light at a single wavelength. One can also observe the polarization of scattered near‐infrared sunlight close to the backscattering direction to obtain information on the water content of the particles. Key Points: Modeled extinction coefficient of marine aerosol depends on particle radius and wavelength, but not on water contentThe depolarization ratio and the color ratio of the backscattering cross section generally decrease with growing aerosol water contentThe linear polarization peak near backscattering angles at NIR wavelength could be used in passive polarimetry to retrieve water content [ABSTRACT FROM AUTHOR]

From a passive satellite remote sensing point of view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of backscattered sunlight at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two such algorithms have demonstrated this capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) observations against AERONET (Aerosol Robotic Network) for common pixels, and for global PARASOL retrievals for the year 2008. For the aerosol optical depth (AOD) over land, both algorithms show a root mean square error (RMSE) of 0.10 (at 550 nm). For single scattering albedo (SSA), both algorithms show a good performance in terms of RMSE (0.04), but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For the Ångström exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSEs of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a root mean square difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33 over land and 0.23 over ocean. For SSA, we find much better agreement over land (bias = - 0.01, RMSD = 0.043 for retrievals with AOD > 0.2) than over ocean (bias = 0.053, RMSD = 0.074). As expected, the differences increase towards low AOD, over both land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the two PARASOL algorithms themselves. Over ocean, the agreement is very similar among the three products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that the dedicated effort in algorithm development for multi-angle polarimetric (MAP) aerosol retrievals still leads to substantial improvement of the resulting aerosol products, and this is still an ongoing process. [ABSTRACT FROM AUTHOR]

This study presents a detailed theoretical assessment of the information content of polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses parameter covariance across the entire parameter space. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing impurities (LAIs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR) and short-wave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAIs in the VIS-NIR, and to grain size at different depths in the snowpack in the NIR-SWIR. When present in the snow or in the atmosphere, LAIs introduce correlated information on grain size in the VIS. Such sensitivity can be disentangled by including multi-spectral and multiangle polarimetric measurements, leading to a better estimate of grain shape and ice crystal roughness, and in turn of the asymmetry parameter which is critical for the determination of albedo. Polarimetry in the SWIR also contains information on aerosol optical thickness while remaining essentially unaffected when the same impurities are embedded in the snow, so that it can effectively partition LAIs between the atmosphere and the surface (a notorious challenge for snow remote sensing based on measurements of total reflectance only) as prospected in a precursor study. The GSA results were used to select state parameters in retrievals performed on data simulated for plausible polar conditions and for multiple instrument configurations. Mono-angle measurements of total reflectance in the VIS-SWIR (akin to MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS-NIR provides information on grain shape and microscale roughness, significantly decreasing the uncertainty in the derived impurity concentration and aerosol optical depth. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors, like those onboard the upcoming PACE and 3MI spaceborne missions. The better characterization of surface and atmospheric parameters in snow-covered regions of the cryosphere ultimately benefits albedo estimates in climate models. [ABSTRACT FROM AUTHOR]

This study presents a detailed theoretical assessment of the information content of polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses parameter covariance across the entire parameter space. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing impurities (LAIs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR) and short-wave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAIs in the VIS-NIR, and to grain size at different depths in the snowpack in the NIR-SWIR. When present in the snow or in the atmosphere, LAIs introduce correlated information on grain size in the VIS. Such sensitivity can be disentangled by including multi-spectral and multi-angle polarimetric measurements, leading to a better estimate of grain shape and ice crystal roughness, and in turn of the asymmetry parameter which is critical for the determination of albedo. Polarimetry in the SWIR also contains information on aerosol optical thickness while remaining essentially unaffected when the same impurities are embedded in the snow, so that it can effectively partition LAIs between the atmosphere and the surface (a notorious challenge for snow remote sensing based on measurements of total reflectance only) as prospected in a precursor study. The GSA results were used to select state parameters in retrievals performed on data simulated for plausible polar conditions and for multiple instrument configurations. Mono-angle measurements of total reflectance in the VIS-SWIR (akin to MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS-NIR provides information on grain shape and microscale roughness, significantly decreasing the uncertainty in the derived impurity concentration and aerosol optical depth. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors, like those onboard the upcoming PACE and 3MI spaceborne missions. The better characterization of surface and atmospheric parameters in snow-covered regions of the cryosphere ultimately benefits albedo estimates in climate models. [ABSTRACT FROM AUTHOR]

With the use of a simple polarimeter, recently introduced at the 60cm telescope of Belogradchik observatory, we obtained our first results on blazar polarimetric variability. Practically all objects we studied showed significant and variable linear polarization as expected from synchrotron emission in a relativistic jet. Observational methods used and some of the first results are presented and discussed. [ABSTRACT FROM AUTHOR]

From a passive satellite remote sensing point-of-view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of back- scattered sun light at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer 5 models. So far, two of such algorithms have demonstrated capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote Sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL observations against AERONET for common pixels, and for global PARASOL retrievals for the year 2008. Both RemoTAP and GRASP show good agreement against AERONET. For the Aerosol 10 Optical Depth (AOD) over land, both algorithms show a Root Mean Square Error (RMSE) of 0.10 (at 550 nm). For Single Scattering Albedo (SSA), both algorithms show a good performance in terms of RMSE ( 0.04) but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For Angstrom Exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSE 15 of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a Root Mean Square Difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33 20 over land and 0.23 over ocean. For SSA, we find good agreement over land (RMSD=0.043) for retrievals with AOD > 0.2. Over ocean the agreement is poor with a bias of 0.053 (where RemoTAP retrieves higher SSA) and an RMSD of 0.074. As expected, the differences increase towards low AOD, both over land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the 2 PARASOL algorithms themselves. Over ocean, the agreement is very similar among 25 the 3 products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. Overall, the good agreement between RemoTAP and GRASP, and between the individual algorithms with AERONET, demonstrates high fidelity of both data products for PARASOL. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that dedicated effort on algorithm development for MAP aerosol retrievals still leads 30 to substantial improvement of the resulting aerosol products and this is still an ongoing process. [ABSTRACT FROM AUTHOR]

Multi-angle polarimetric (MAP) measurements contain rich information for characterization of aerosol microphysical and optical properties that can be used to improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system, although uncertainty correlation among measurements is generally ignored due to lack of knowledge on its strength and characterization. In this work, we provide a practical framework to evaluate the impact of the angular uncertainty correlation from retrieval results and a method to estimate correlation strength from retrieval fitting residuals. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on neural-network forward models, is used to conduct the retrievals and uncertainty quantification. In addition, we also discuss a flexible approach to include a correlated uncertainty model in the retrieval algorithm. The impact of angular correlation on retrieval uncertainties is discussed based on synthetic Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) and Hyper-Angular Rainbow Polarimeter 2 (HARP2) measurements using a Monte Carlo uncertainty estimation method. Correlation properties are estimated using autocorrelation functions based on the fitting residuals from both synthetic AirHARP and HARP2 data and real AirHARP measurement, with the resulting angular correlation parameters found to be larger than 0.9 and 0.8 for reflectance and degree of linear polarization (DoLP), respectively, which correspond to correlation angles of 10 and 5 ∘. Although this study focuses on angular correlation from HARP instruments, the methodology to study and quantify uncertainty correlation is also applicable to other instruments with angular, spectral, or spatial correlations and can help inform laboratory calibration and characterization of the instrument uncertainty structure. [ABSTRACT FROM AUTHOR]

In ship detection by means of Polarimetric SAR imagery, a very promising feature is the characterization of the pixels of the ship based on the elementary scattering mechanisms that can be extracted using different decomposition algorithms. Elementary scattering mechanisms provide information regarding the physical, electrical and geometrical properties of the scatterers in each Polarimetric SAR pixel. In this work, the newly established algorithm of the Double Scatterer Model is applied to interpret each pixel of the Polarimetric SAR image with the contributions of two elementary scattering mechanisms, namely, primary and secondary. The main idea is to construct a binary image while preserving the rich information content in order to proceed in simple and fast image processing for target detection. The present algorithm is applied to datasets with different inherent characteristics acquired by Radarsat-2 and ALOS-PALSAR. The results presented by this new perspective on ship monitoring are remarkable. [ABSTRACT FROM AUTHOR]

The complex precipitation microphysics associated with super typhoon Lekima (2019) and its potential impacts on the consistency of multi-source datasets and radar quantitative precipitation estimation were disentangled using a suite of in situ and remote sensing observations around the waterlogged area in the groove windward slope (GWS) of Yandang Mountain (YDM) and Kuocang Mountain, China. The main findings include the following: (i) the quality control processing for radar and disdrometers, which collect raindrop size distribution (DSD) data, effectively enhances the self-consistency between radar measurements, such as radar reflectivity (ZH), differential reflectivity (ZDR), and the specific differential phase (KDP), as well as the consistency between radar, disdrometers, and gauges. (ii) The microphysical processes, in which breakup overwhelms coalescence in the coalescence–breakup balance of precipitation particles, noticeably make radar measurements prone to be breakup-dominated in radar volume gates, which accounts for the phenomenon where the high number concentration rather than the large size of drops contributes more to a given attenuation-corrected ZH (ZHC) and the significant deviation of attenuation-corrected ZDR (ZDRC) from its expected values (Z^DR) estimated by DSD-simulated ZDR – ZH relationships. (iii) The twin-parameter radar rainfall estimates based on measured ZH (ZHM) and ZDR (ZDRM), and their corrected counterparts ZHC and ZDRC , i.e., R (ZHM , ZDRM) and R (ZHC , ZDRC), both tend to overestimate rainfall around the GWS of YDM, mainly ascribed to the unique microphysical process in which the breakup-dominated small-sized drops above transition to the coalescence-dominated large-sized drops falling near the surface. (iv) The improved performance of R (ZHC , Z^DR) is attributed to the utilization of Z^DR , which equals physically converting breakup-dominated measurements in radar volume gates to their coalescence-dominated counterparts, and this also benefits from the better self-consistency between ZHC , Z^DR , and KDP , as well as their consistency with the surface counterparts. [ABSTRACT FROM AUTHOR]

Polarimetric microphysical retrievals reveal a great potential for the evaluation of numerical models and data assimilation. However, the accuracy of ice microphysical retrievals is still poorly explored. To evaluate these retrievals and assess their accuracy, polarimetric radar measurements are spatially and temporally collocated with in situ aircraft measurements obtained during the OLYMPEX campaign (Olympic Mountain Experiment). Retrievals for ice water content IWC, total number concentration Nt , and mean volume diameter Dm of ice particles are assessed by comparing an in situ dataset obtained by the University of North Dakota (UND) Citation II aircraft with X-band Doppler on Wheels (DOW) measurements. Sector averaged range height indicator (RHI) scans are used to derive vertical profiles of microphysical retrievals. The comparison of these estimates with in situ data provides insights into strengths, weaknesses, and accuracy of the different retrievals, and quantifies the improvements of polarimetry-informed retrievals compared to conventional, non-polarimetric ones. In particular, the recently introduced hybrid ice water content retrieval exploiting reflectivity Z H, differential reflectivity Z DR and specific differential phase K DP outperforms other retrievals based on either (Z H, Z DR) or (ZH, K DP) or non-polarimetric retrievals in terms of correlations with in situ measurements and the root mean square error. [ABSTRACT FROM AUTHOR]

Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. [ABSTRACT FROM AUTHOR]

Multi-angle polarimetric (MAP) measurements contain rich information for characterization of aerosol microphysical and optical properties that can be used to improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere-ocean system, although uncertainty correlation among measurements is generally ignored due to lack of knowledge on its strength and characterization. In this work, we provide a practical framework to evaluate the impact of the angular uncertainty correlation from retrieval results and a method to estimate correlation strength from retrieval fitting residuals. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on neural network forward models, is used to conduct the retrievals and uncertainty quantification. In addition, we also discuss a flexible approach to include a correlated uncertainty model in the retrieval algorithm. The impact of angular correlation on retrieval uncertainties is discussed based on synthetic AirHARP and HARP2 measurements using a Monte Carlo uncertainty estimation method. Correlation properties are estimated using auto-correlation functions based on the fitting residuals from both synthetic AirHARP and HARP2 data and real AirHARP measurement, with the resulting angular correlation parameters found to be larger than 0.9 and 0.8 for reflectance and DoLP, respectively, which correspond to correlation angles of 10° and 5°. Although this study focuses on angular correlation from HARP instruments, the methodology to study and quantify uncertainty correlation is also applicable to other instruments with angular, spectral, or spatial correlations, and can help inform laboratory calibration and characterization of the instrument uncertainty structure. [ABSTRACT FROM AUTHOR]

Quantitative estimations of atmospheric aerosol absorption are rather uncertain due to the lack of reliable information about the global distribution. Because the information about aerosol properties is commonly provided by single-viewing photometric satellite sensors that are not sensitive to aerosol absorption. Consequently, the uncertainty in aerosol radiative forcing remains one of the largest in the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC AR5 and AR6). Here, we use multi-angular polarimeters (MAP) to provide constraints on emission of absorbing aerosol species and estimate global aerosol absorption optical depth (AAOD) and its climate effect. Our estimate of modern-era mid-visible AAOD is 0.0070 that is higher than IPCC by a factor of 1.3-1.8. The black carbon instantaneous direct radiative forcing (BC DRF) is +0.33 W/m2 [+0.17, +0.54]. The MAP constraint narrows the 95% confidence interval of BC DRF by a factor of 2 and boosts confidence in its spatial distribution. Chen et al. demonstrate the use of multi-angular polarimetric remote sensing to pinpoint global aerosol absorption and direct radiative forcing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere-ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and nonlinearity of radiative transfer near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on several neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the technique to uncertainty evaluation in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, and guide MAP algorithm development for current and future satellite systems such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most existing cloud models tend to overestimate the size of cloud ice particles and underestimate their concentration. This emphasizes the need to provide a reliable observational reference to optimize cloud model performance, particularly in areas of high concentration of ice at high altitudes. The dual‐polarization radars give the community a unique opportunity to quantify cloud ice with a good accuracy using polarimetric radar retrievals. In this study, we utilize the network of operational WSR‐88D radars to build a climatology of the vertical profiles of radar variables, such as radar reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP as well as the radar‐retrieved vertical profiles of ice water content (IWC) above the melting layer and liquid water content below it, mean volume diameter Dm, and total number concentration Nt of ice and liquid particles. Such climatology was created for continental/marine mesoscale convective systems (MCSs) and tropical cyclones including hurricanes. The dataset includes 13 continental MCSs, 10 marine MCSs, and 11 tropical cyclones. Separate statistics of the "background" vertical profiles and the ones associated with high IWC aloft have been obtained in the course of this study. It is shown that continental MCSs exhibit larger size of ice in lower concentration aloft compared to the marine MCSs and especially tropical cyclones/hurricanes. A combination of high KDP and low Z aloft signifies lower Dm, higher Nt, and often substantial IWC. Key Points: Climatology of the vertical profiles of polarimetric radar variables and microphysical parameters is documented for different storm typesAreas of high ice water content (IWC) in clouds are characterized by high values of specific differential phase combined with low reflectivityTropical clouds have smaller size ice in higher concentration compared to their continental counterparts with comparable IWC [ABSTRACT FROM AUTHOR]

Within the maximum likelihood method an optimal algorithm for polarization target selection against the background of interfering signal reflected from the earth's surface is synthesized. The algorithm contains joint operations of spectral interference rejection and their polarization compensation by means of certain combinations of interchannel subtraction of signals of different polarizations. The physical features of the elements of the polarization scattering matrix are investigated for the technical implementation of the synthesized algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In variable polarimetric monopulse radars, due to the number and types of data channels, the polarisation and angle information of the target in the receiving signals are non‐linearly coupled, which results in incorrect polarisation measurement and erroneous angle estimation through a linear digital beam forming (DBF) method. Based on a variable polarimetric monopulse radar with four channels, two non‐linear DBF methods are proposed to improve radar ability. Through the angle monopulse estimation principle and the reciprocity theorem, new pairs of summation and difference signals in all polarisation channels are formed to generate the correct polarimetric scattering and angle estimation results. The simulation and experiment results show that the proposed methods achieve a higher precision of polarisation measurement and more consistent angle estimation results compared to the linear DBF method. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imaging has been an exciting field owing to its wide application in military/non military fields. This paper is aimed at investigating effects of using different types of signals and emission parameters on the accuracy and resolution of simulated SAR image and performing simulation on a reconstructed SAR image of an fully polarimetric SAR for identifying different land features. The speckles in the image are removed using the Lee filter and then the image is decomposed using Pauli decomposition method. After feature extraction, neural network techniques are used for training, validating and testing the network. The final image classified in RGB, representing features like urban, vegetation and water has been achieved. The percentage classification of each feature pixels in the image has been presented using confusion matrix. [ABSTRACT FROM AUTHOR]

Rapid-scan polarimetric data analysis of the dissipation of a likely violent supercell tornado that struck near Sulphur, Oklahoma, on 9 May 2016 is presented. The Rapid X-band Polarimetric Radar was used to obtain data of the tornado at the end of its mature phase and during its entire dissipation phase. The analysis is presented in two parts: dissipation characteristics of the tornadic vortex signature (TVS) associated with the tornado and storm-scale polarimetric features that may be related to processes contributing to tornado dissipation. The TVS exhibited near-surface radial velocities exceeding 100 m s−1 multiple times at the end of its mature phase, and then underwent a two-phased dissipation. Initially, decreases in near-surface intensity occurred rapidly over a ~5-min period followed by a slower decline in intensity that lasted an additional ~12 min. The dissipation of the TVS in time and height in the lowest 2 km above radar level and oscillatory storm-relative motion of the TVS also are discussed. Using polarimetric data, a well-defined low reflectivity ribbon is investigated for its vertical development, evolution, and relationship to the large tornadic debris signature (TDS) collocated with the TVS. The progression of the TDS during dissipation also is discussed with a focus on the presence of several bands of reduced copolar correlation coefficient that extend away from the main TDS and the eventual erosion of the TDS as the tornado dissipated. Finally, TVS and polarimetric data are combined to argue for the importance of a possible internal rear-flank downdraft momentum surge in contributing to the initial rapid dissipation of the tornado. [ABSTRACT FROM AUTHOR]

Simulations of the radiometric and polarimetric properties of ice clouds based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 ice cloud model (aggregates in conjunction with the gamma distribution) are performed at wavelengths in the Infrared (IR) and Submillimeter/Millimeter (sub‐mm/mm) regimes. The scattering/absorption/polarization properties from the precalculated database are incorporated into a radiative transfer model, the Atmospheric Radiative Transfer Model (ARTS). A number of sub‐mm/mm bands have been considered from radiometric and polarimetric studies for potential applications to retrieving ice cloud properties at shorter sub‐mm/mm wavelengths (higher frequencies). The IR atmospheric window has also been thoroughly investigated for its feasibility to observe ice clouds. However, in the literature there is a lack of studies utilizing these high frequency bands or polarimetric IR observations to infer ice cloud properties. This study focuses on performing simulations of ice clouds between 680 and 183 GHz (sub‐mm/mm regime) and between 8.6 and 12 μm wavelengths (IR‐window regime) toward determining potential applications to simultaneous retrievals of ice water path (IWP) and effective diameter (Deff) with a primary focus on optically thin cirrus clouds. IWP and Deff isoline plots (lookup tables) are created to conduct sensitivity analyses and to perform preliminary retrievals. Results reveal that the utilization of both sub‐mm/mm and IR wavelengths combined with linearly polarized and unpolarized measurements leads to the most accurate IWP and Deff retrievals with errors lower than 10%. Key Points: IR wavelengths mainly sensitive to optically thin ice clouds while sub‐mm/mm wavelengths mainly sensitive to optically thick cloudsSub‐mm/mm wavelengths corresponding to higher frequencies (>600 GHz) could be used to retrieve properties from optically thin cloudsLinearly polarized sub‐mm/mm and IR wavelengths have the potential to retrieve ice water path and effective diameter [ABSTRACT FROM AUTHOR]

Water polarization characteristics Liu et al. ([3]) investigate the possibility of retrieving suspended particulate matter in turbid waters using polarization properties and radiative transfer simulations. Atmosphere polarization characteristics Chen et al. ([1]) show that different aerosols in atmosphere produce different polarization patterns. The polarimetric feature extractor mainly provides distribution characteristics of polarization information, including degree of polarization and angle of polarization. [Extracted from the article]

Polarization is the intrinsic property of electromagnetic wave and has not been fully investigated in remote sensing field. The incident solar radiation is polarized by the components in the atmosphere and shows stable patterns. Therefore, the sky polarization could be served as an important information source for navigation. However, how different factors affecting the variations of sky polarization pattern is still a complex question, making it difficult to apply sky polarization pattern in navigation. Aerosol is one of the major sources for this uncertainty. Current studies mainly neglect aerosol or just consider aerosol optical depth (AOD), without considering microphysical properties of aerosol. In this study, we perform the influences of both aerosol mode and AOD on the trends of sky polarization pattern. The results indicate that scattering aerosol mode and absorbing aerosol mode have different effects on sky polarization pattern. Over ocean surface, scattering aerosol tends to decrease the magnitude of sky polarization pattern, while absorbing aerosol tends to increase that. Over land surface, both aerosol modes tend to decrease the magnitude of sky polarization, and scattering aerosol mode is more effectively. The discoveries in our study suggest that to utilize sky polarization pattern information, it is necessary to get detailed aerosol information especially the aerosol mode information. [ABSTRACT FROM AUTHOR]

In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re : R=0.756 , RMSE=1.74µm) and cloud optical thickness (τ : R=0.950 , RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R=0.54 , RMSE=4.77µm) and τ (R=0.785 , RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset. [ABSTRACT FROM AUTHOR]

Hypotheses have been proposed for decades about the effect of activated cloud condensation nuclei (CCN) on delaying the warm rain process, invigorating deep convective cloud vertical development, and enhancing mixed‐phase processes. Observational support has been only qualitative with mixed results due to the lack of regional measurements of CCN concentration (NCCN), while simulations have not produced a robust consensus. Quantitative assessments of these relationships became possible with the advent of NCCN retrievals from satellites; when combined with measurements by polarimetric radar and Lightning Mapping Array (LMA), tracking convective cells observed on radar and examining precipitation properties throughout the cells' life cycle has permitted the study of the impact of NCCN on cloud and precipitation characteristics. We composited more than 2,800 well‐tracked cells in the Houston region and stratified them by NCCN, convective available potential energy (CAPE), and urban/rural locations. The results show that increased NCCN invigorates the convection until saturation near NCCN = 1,000 cm3; increasing NCCN from ~400 to an optimum of ~1,000 cm3 increases lightning activity by an order of magnitude. A further increase in CCN decreases lightning rates. Adding CAPE enhances lightning only under low NCCN (e.g., less than 500 cm3). The presence of the urban area enhances lightning for similar NCCN concentrations, although this applies mainly under low NCCN conditions. The urban heat island as manifested by cloud base height cannot explain this observation. It is suspected that the urban ultrafine aerosols contribute to the storm electrification. Plain Language Summary: Deep convective clouds are propelled by rising air currents and are composed of cloud droplets that nucleate on CCN aerosols. Isolating the effects of CAPE and NCCN on cloud properties has been an unresolved challenge. Tracking the time‐height evolution of a large number of individual summer convective storm cells in the Houston area under various CAPE and NCCN shows their relations to the storm's dynamics, precipitation, and electrification processes. The results show that increased NCCN invigorates the convection, produces larger hydrometeors, and enhances lightning. Variability in NCCN was found to be more important than variability in CAPE, cloud base height, and wind shear in explaining the variability of the vigor and electrification of deep convective clouds in the study area. Key Points: Increased CCN invigorates convection, produces larger hydrometeors, and enhances lightningCCN invigoration effect saturates near NCCN = 1,000 cm3 in an environment with similar CAPECCN is more important than CAPE in explaining the variability of the vigor and electrification of storms after convection is initiated [ABSTRACT FROM AUTHOR]

Compact and lightweight photodetection elements play a critical role in the newly emerging augmented reality, wearable and sensing technologies. In these technologies, devices are preferred to be transparent to form an optical interface between a viewer and the outside world. For this reason, it is of great value to create detection platforms that are imperceptible to the human eye directly onto transparent substrates. Semiconductor nanowires (NWs) make ideal photodetectors as their optical resonances enable parsing of the multi-dimensional information carried by light. Unfortunately, these optical resonances also give rise to strong, undesired light scattering. In this work, we illustrate how a new optical resonance arising from the radiative coupling between arrayed silicon NWs can be harnessed to remove reflections from dielectric interfaces while affording spectro-polarimetric detection. The demonstrated transparent photodetector concept opens up promising platforms for transparent substrates as the base for opto-electronic devices and in situ optical measurement systems. For augmented reality technologies it is beneficial to create devices on transparent substrates that are imperceptible to the human eye. Here, the authors harness resonances from radiative coupling between arrayed silicon nanowire photodetectors to remove reflections while affording spectro-polarimetric detection. [ABSTRACT FROM AUTHOR]

In this study, the authors consider the problem of moving target detection in the presence of heterogeneous clutter. The knowledge-aided method has been extended to derive the polarimetric multiple-input multiple-output (MIMO) radar detector. And the polarimetric clutter covariance matrix was modelled as a complex inverse Wishart distribution. More precisely, the clutter covariance matrices between different transmitter–receiver (Tx–Rx) pairs are assumed to follow the distribution which shares a prior clutter covariance matrix structure while with different values of the power levels. Under this assumption, the generalised likelihood ratio test (GLRT) approach has been adopted to develop the polarimetric knowledge-aided detector without secondary data. Numerical results are shown to illustrate the effectiveness of the polarimetric knowledge-aided GLRT detector in heterogeneous clutter. [ABSTRACT FROM AUTHOR]

A novel method for the design of random radar signal with low sidelobe is proposed in this study. Firstly, the authors modulate the amplitude spectrum of the designed radar signal using a Gaussian window and phase spectrum using a random sequence. Then the inverse Fourier transformation is adopted to obtain the time-domain designed random radar signal. The random radar signal possesses low sidelobe thanks to its Gaussian power spectral density and random waveform owing to its random phase spectrum. The performance of the designed random radar signal is examined by both numerical simulation and real data experiment with dual-polarisation radar, where the polarimetric micro-Doppler of a pedestrian is demonstrated. [ABSTRACT FROM AUTHOR]

The purpose of this study is to demonstrate that the fully polarimetric radar can be effectively used to measure the insect orientation. First, the polarisation scattering matrix (PSM)-based orientation extraction method is reviewed. This method was proposed for traditional rotation polarisation radar, but is also applicable to fully polarimetric radar. Then, both anechoic chamber and field experiments were conducted to measure fully polarimetric insect PSMs and orientations, which verified the effectiveness of insect orientation extraction based on fully polarimetric measurement. [ABSTRACT FROM AUTHOR]

(Dis)similarity measures play an important role in the interpretation of polarimetric synthetic aperture radar (PolSAR) images. Here, the authors introduce a kind of similarity measures for PolSAR images based on the concepts of Hölder pseudo-divergence and Hölder divergence. Authors' similarity measures are more generalised as the derived formulas indicate that they contain several widely used measures, such as Bartlett or Bhattahcharyya distances and Chernoff distance. Also, their similarity measures are derived from the complex Wishart distribution, so these measures are good at quantifying the similarity between two covariance matrices and perform well while dealing with classification problems for PolSAR images. Experimental results on unsupervised and supervised classification of PolSAR images also verify the effectiveness of their measures. [ABSTRACT FROM AUTHOR]

Scattering entropy (H), scattering angle (α) and anti-entropy (A) are useful parameters in synthetic aperture radar (SAR) image classification. Usually, full-polarimetric SAR data are needed to extract these parameters. In this study, the authors firstly try to predict these parameters from single/dual-polarimetric SAR data using convolutional neural network. Experiments are done on GF-3 polarised SAR database, and promising results are obtained, where the parameters H and α, the average relative error reached is <10%, the parameter A, the average relative error reached is around 25%, and the classification performance based on predictive parameters is around 80%. Furthermore, the predicting performance using different single- and dual-polarisation is compared. The results and conclusions provide a new clue for the applications of single/dual-polarimetric SAR. [ABSTRACT FROM AUTHOR]

The fully polarimetric synthetic aperture radar (SAR) data contain much backscatter intensity information and have great advantage for land monitoring. Analysing the polarisation features of typical targets is a critical step for landing monitoring, especially the ground objects identification, and thus accurately obtaining and analysing the polarisation features is of importance. Gaofen-3(GF-3) is the first C band fully polarimetric SAR satellite in China, which is widely used in various fields such as land monitoring, disaster reduction and so on. Here, the GF-3 fully polarimetric SAR data at three time-slices were used to obtain and analyse the polarisation features of typical targets in Chengdu. The results revealed the scattering type of the typical targets including artificial buildings and vegetation and indicated that the GF-3 SAR data were suitable to be used for obtaining polarisation features of typical targets and have the potential for discriminate artificial building and further for landing monitoring. [ABSTRACT FROM AUTHOR]

The backscattering of radar targets is sensitive to the relative geometry between targets' orientations and the radar. This target scattering diversity makes mechanism interpretation and quantitative application difficult. This study reviews a newly established theory for target scattering mechanism interpretation in the rotation domain. It contains uniform polarimetric matrix rotation theory and the interpretation tool of polarimetric coherence pattern. The core idea of the proposed method is to expand targets' information obtained at a specific orientation to the full rotation domain, which can provide fundamentals for the deep mining and utilisation of target scattering information. On this basis, typical applications with the derived new polarimetric features are introduced and the effectiveness of these features is demonstrated. [ABSTRACT FROM AUTHOR]

Many previous studies have shown that multiangle, multispectral polarimetric remote sensing can provide valuable information on aerosol microphysical and optical properties, in which the aerosol layer height (ALH) is an important parameter but with less studies, especially in the near-ultraviolet (near-UV) and visible (VIS) wavelength bands. Based on the optimal estimation (OE) theory and information content analysis method, we focus on the sensitivity study of ALH with the synthetic data in the near-UV and VIS wavelength in the range of 410–865 nm, and further to assess the capability of multiangle intensity and polarization measurements for the retrieval of ALH. Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) has been used as the forward model to simulate the intensity and polarized radiance at the top of atmosphere (TOA), as well as the Jacobians of TOA results with respective to corresponding parameters. The degree of freedom for signal (DFS) and a posteriori error are introduced to quantity the information content of ALH from the intensity and polarization measurements, respectively. By assuming the surface type, aerosol model, aerosol loads, prior errors and observation geometries, the sensitivity of ALH has been preliminarily investigated. The sensitivity study results show that the near-UV and polarization measurements are the important source of information content for the aerosol height retrieval in satellite remote sensing. [ABSTRACT FROM AUTHOR]

Thanks to the recent advances in the development of polarimetric synthetic aperture radar (SAR) sensors, this remote sensing field attracts many applications. Among the different applications of these data, change detection is one of the most important applications. PolSAR images, due to interactions between electromagnetic waves and the target, could be used to study changes in the Earth's surface. This paper is a type of transformation-based method for polarimetric change detection (CD) purpose. For this purpose, we use full polarimetry imaging radar and extracted 138 features based on decomposition. The CD methods are the principal component analysis (PCA), the Multivariate Alteration Detection (MAD), the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), the Covariance Equalization (CE), and the Cross-Covariance (CRC). Assessment of the incorporated methods performed using most common criteria as quantity and quality assessment, such as overall accuracy (OA), kappa coefficient, and as visual analysis. The results of the experiments show that CC has better performance compared with other algorithms. [ABSTRACT FROM AUTHOR]

Tropical forest is important in tackling global climate change. However, large-scale transformation of tropical forest in Indonesia is being done for oil palm plantation. Till date, there is a lack of scientific documentation that shows the most recent situation of oil palm plantation in the most eastern part of the country. This study documents the real condition of oil palm plantation expansion based on Sentinel data of European Space Agency (ESA). Sentinel-1 data sets of Papua Provinces of the year 2017 acquisition are used to avoid cloud cover problems in tropical regions. Object-based geospatial data feature extraction with Polarimetric SAR compared to the multiple classifier methods (that is, Gaussian Mixture Method, Random Forest, Support Vector Machine, and K-Nearest Neighbors) were introduced and employed using GRASS & QGIS of free open source software (FOSS). Google Earth Engine (GEE) was then used to verify the classification result of oil palm plantation. The preprocessing step is very crucial in achieving high accuracy. However, it is time-consuming. This study concluded that the proposed method and SVM classifiers achieved the highest accuracy, 99.7% and 99.12%, respectively. While the KNN, RF, and GMM classifier produced 94.63%, 89.06%, and 72.87%, respectively. We found that vast scale transformation of tropical rainforest in lowland areas is happening in eastern Indonesia. This situation should be strictly monitored and controlled to avoid more significant issues related to socio-economic-environmental implications. [ABSTRACT FROM AUTHOR]

The purpose of this study is to introduce efficient bio‐inspired vision architectures, integrating human cognition capabilities, such as computation and memory emulating neurons and synapses, together with polarization of light properties, that would ultimately revolutionize and give rise to the next‐generation highly efficient augmented intelligence vision systems. The system consists of a polarimetric dynamic vision sensor) and a spinning wheel, whose motion is externally modulated and controlled by means of a signal generator, varying its motion pattern and speed, respectively. A neural network has been designed to classify different object speeds and motion patterns. The neural network utilizes a limited number of events within a given time window, instead of full‐frame images; as well as two classifiers, which practically take a single input and independently classify both its speed and motion pattern. As a result, both high computational efficiency and motion classification accuracy are achieved. [ABSTRACT FROM AUTHOR]

Binary pulsars are affected by general relativity (GR), causing the spin axis of each pulsar to precess. We present polarimetric radio observations of the pulsar PSR J1906+0746 that demonstrate the validity of the geometrical model of pulsar polarization. We reconstruct the (sky-projected) polarization emission map over the pulsar’s magnetic pole and predict the disappearance of the detectable emission by 2028. Two tests of GR are performed using this system, including the spin precession for strongly self-gravitating bodies. We constrain the relativistic treatment of the pulsar polarization model and measure the pulsar beaming fraction, with implications for the population of neutron stars and the expected rate of neutron star mergers. [ABSTRACT FROM AUTHOR]

In this study we developed a neural network (NN) that can be used to relate a large dataset of multi-angular and multi-spectral polarimetric remote sensing observations to retrievals of cloud microphysical properties. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to Research Scanning Polarimeter (RSP) data obtained during the ORACLES field campaign (Observations of Aerosols above Clouds and their Interactions). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first-guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals - such as when an aerosol layer lies above clouds like in ORACLES. For the ORACLES 2016 dataset comparisons of the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re : R=0.756, RMSE=1.74μm) and cloud optical thickness (τ : R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely the the polarimetric cloud retrieval and the bispectral total reflectance cloud retrieval. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R=0.54, RMSE=4.77μm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately - but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm, for polarimetric synthetic aperture radar (PolSAR) classification, based on a stacked auto-encoder and scattering energy. Previous approaches to PolSAR classification predominantly consider only the single pixel of distribution of the polarimetric data and scattering characteristics, and ignore other kinds of image features like the relationship of the local pixels. Besides, because of the complexities of PolSAR data, it is difficult to compute the derivatives that are needed for back-propagation in deep-learning classifiers. To overcome these difficulties, we propose a new approach that combines the scattering power and stacks sparse auto-encoder (Scattering SSAE) for PolSAR classification. Firstly, orientation compensation is used to compensate the polarization orientation angle, reducing the impact of polarimetric angle noise. Secondly, Freeman-Durden decomposition is adopted to extract three basic scattering powers: surface, double bounce and volume. Each PolSAR image pixel is transformed into these scattering powers, yielding a new kind of feature from the PolSAR data. Finally, using the three kinds of scattering power as inputs, we combine local spatial information using a patch-based approach, and use a deep learning architecture to achieve classification. We compare our method against several other state-of-the-art methods using ground-truthed test-data, and show that the Scattering SSAE method achieves higher accuracy than other methods on most categories. [ABSTRACT FROM AUTHOR]